Determining ambient noise levels in the presence of construction is a difficult and time consuming task, and the lack of dedicated analysis and visualisation tools can lead to much disagreement between interested parties about typical ambient levels. This paper presents an approach inspired by previous work on cluster-based analysis and visualisation of daily energy usage patterns, and demonstrates substantial success in achieving the aim of establishing typical ambient noise levels, as well as raising further questions about what typical truly means.

1. Determining Typical Ambient Noise Levels in the Presence of
Construction
Vahndi Minah
Abstract—Determining ambient noise levels in the presence of construction is a difficult and time consuming task, and the lack of
dedicated analysis and visualisation tools can lead to much disagreement between interested parties about typical ambient levels.
This paper presents an approach inspired by previous work on cluster-based analysis and visualisation of daily energy usage patterns,
and demonstrates substantial success in achieving the aim of establishing typical ambient noise levels, as well as raising further
questions about what typical truly means.
Index Terms—Environmental noise, construction noise, clustering, visualisation.
INTRODUCTION
It has become fairly standard practice for large construction projects in the UK,
such as CTRL [1], Crossrail [2], and Thames Tideway Tunnel [3], to provide
compensation for nearby businesses and residents who are predicted to be
adversely affected by construction works, even after the application of
mitigation using “Best Practicable Means”, as defined in Section 72 of the
Control of Pollution Act 1974 [4]. This compensation is typically noise and
vibration mitigation packages in the form of secondary glazing and / or
temporary rehousing. Table 1 shows the Noise Insulation Trigger Level Table
from the Crossrail project’s noise and vibration mitigation scheme, which is
set out in Information Paper D9 (IP D9) [5]. The time periods are typically
chosen to reflect site start-up and shut-down hours (Periods 1, 3, 6 and 8), core
working hours (Periods 2 and 7), hours of relaxation (Periods 4, 9 and 11) and
hours of sleeping (Periods 5, 10 and 12).
Day Period
Number
Relevant
Time Period
Averaging
Time T
Noise
Insulation
Trigger
Level dB
LAeq, T
Monday to
Friday
1 07:00 – 08:00 1 hr 70
2 08:00 – 18:00 10 hrs 75
3 18:00 – 19:00 1 hr 70
4 19:00 – 22:00 3 hrs 65
5 22:00 – 07:00 1 hr 55
Saturday 6 07:00 – 08:00 1 hr 70
7 08:00 – 13:00 10 hrs 75
8 13:00 – 14:00 1 hr 70
9 14:00 – 22:00 3 hrs 65
10 22:00 – 07:00 1 hr 55
Sunday 11 07:00 – 21:00 1 hr 65
12 21:00 – 07:00 1 hr 55
Table 1 - Noise Insulation Trigger Levels from the Crossrail Noise and
Vibration Mitigation Scheme
Where works take place in close proximity to a large number of noise- or
vibration- sensitive receptors, the costs of providing this mitigation can quickly
escalate (a typical secondary glazing and ventilation package is estimated to
cost around £300 per affected window). Provision of noise mitigation under
these projects' noise and vibration mitigation schemes is typically dependent
on the difference between the prevailing ambient noise level in the area before
construction commences, and the predicted and/or actual noise levels from
construction once works commence.
Therefore there is a large benefit both to project sponsors and to local
businesses and residents, in ensuring that the measured pre-construction
ambient levels are sufficient and correct, in advance of the construction works
taking place. In the worst case scenarios, if actual ambient noise levels are
found to be incorrect whilst works are taking place, this can potentially lead to
extra uncosted noise mitigation being required. This could mean either the
works being stopped while the installation of mitigation takes place (at a
potential cost of millions of pounds for large projects on sites with programme-
critical works), or the provision of temporary rehousing for residents, which
may come at great cost to their quality of life, and large financial costs to the
project.
However, despite the risks in not sufficiently ascertaining the prevailing
ambient noise levels at each location, the environmental assessment packages
of work required to obtain planning permission and funding for large projects
are usually put out to competitive tender, often later than would be ideal; this
can lead to costs being cut during value engineering stages, and shorter, less
comprehensive noise surveys being carried out.
Moreover, there is an incentive for local authorities, who are answerable to
their local residents and businesses, to argue that pre-construction surveys were
in some way insufficient or inaccurate if it can secure additional compensation
from the project for their residents and businesses.
The author’s professional experience on these and other large construction
projects has identified a need for more efficient methods to determine the most
likely levels of ambient noise after construction has commenced. Therefore,
this report describes a new software program written to address this need, and
associated analysis tasks which have been conducted in order to ascertain the
ambient noise level at a construction noise monitor from the Crossrail Project.
1 DATA
Sections 1.1 and 1.2 section define a number of acoustics-related terms which
readers may be unfamiliar with. Whilst a brief explanation of the relevant terms
are given, readers requiring more detailed explanations are encouraged to
follow the hyperlinks provided for further information. The author apologises
for the introduction of these terms, but they will be useful for understanding
some of the later points in the analysis.
1.1 Acoustic Theory
Noise is generally defined as ‘unwanted sound’. Therefore ‘noise’ has
historically been used in the field of environmental acoustics, and ‘sound’ has
been used more in the field of architectural acoustics, or to describe acoustic
pressure fluctuations in the more general sense. However there has recently
been a push within the field of environmental acoustics to not be so
presumptuous about the desirability of environmental sound; a certain level of
broadband noise can be desirable (as anyone who has felt the unnerving effect
of being in an anechoic chamber can attest to). In the context of this report,
‘noise’ will generally be used, consistent with the wording of the current
relevant legislation on construction and environmental noise [6] [7].
1.1.1 Level
Noise levels are measured using the decibel (dB) scale. This is a scale which is
logarithmic in the sound pressure levels, and is used because the mammalian
auditory system responds to changes in acoustic pressure in a logarithmic way.
The original scale, named after Alexander Graham Bell, sets out that a one Bel
change corresponds to a ten-fold increase in sound pressure levels, but since
this scale is too coarse for everyday use, the decibel scale is used where each
decibel is one tenth of a Bel.
The level of any sound is calculated by comparing the pressure to a reference
sound pressure level of 0 dB (pref = 20 µPa.), which is the threshold of human
hearing. The equation for calculating the level of a sound with sound pressure
level p is as follows:

2. L = 10 × log10
p
pref
1.1.2 Frequency
The frequency of sound is the rate at which pressure waves oscillate about the
standard atmospheric pressure, and is measured in Hertz (Hz). 1 Hz
corresponds to an oscillation of one cycle per second. The frequency range of
human hearing is from approximately 20 Hz to 20 kHz for a young adult with
good hearing, although the upper range decreases markedly with age, as
damage is done to the cochlea cells in the inner ear.
Frequency is loosely related to musical pitch. The mammalian auditory system
also responds to frequency in a logarithmic fashion. Plainly put, a doubling in
the frequency of a sound corresponds to a one octave increase in the pitch. Pure
tones consist of a single frequency. Musical instruments typically have most of
their energy at the fundamental frequency and integer multiples of it, which is
what gives them their specific timbre. Noise is generally ‘broadband’,
consisting of energy distributed across the audible spectrum.
1.1.3 Sound Propagation
The level of a sound decays with distance from the emitting source. The rate
of decay is dependent on the type of source generating the sound. Sound from
an object acting as a point source, for example a small piece of machinery, is
attenuated at a rate of 6 dB per doubling of distance from the source. Sound
from an object acting as an infinite line source, for example a road with a steady
stream of traffic, is attenuated at a rate of 3 dB per doubling of distance. In
reality, there are no infinite line sources; however, the 3 dB rate of attenuation
is a good approximation which is commonly used.
Sound is also attenuated by objects between the source and receiver location
acting as noise barriers. When the barrier object partially obscures the line of
sight from the source to the receiver, the sound is attenuated by approximately
5 dB. When the line of sight is completely blocked, the sound is attenuated by
approximately 10 dB. Up to 20 dB of attenuation can be achieved in theory;
however, the material used for noise barriers on construction sites is typically
not of sufficient mass or adequacy of construction to prevent transmission of
sound through the barrier, so 10 dB of attenuation is a realistic upper limit when
dealing with construction noise.
1.1.4 Rules of thumb
Some handy rules of thumb for thinking about sound are:
 A 1 dB change corresponds to a just-noticeable difference in the level of
a pure tone.
 A 3 dB change corresponds to a noticeable difference in the level of
broadband noise (broadband noise consists of sound across the frequency
spectrum.
 A 10 dB increase in sound pressure level corresponds to a subjective
doubling in perceived loudness of the sound.
 A 3 dB increase, i.e. a doubling of the acoustic energy in the sound, is
generally considered a significant increase and is used as the level at
which environmental noise impacts are identified.
 A 5 dB increase is sometimes used to trigger mitigation for construction
noise, since the effect of a relatively short term increase in noise level is
considered lesser than a permanent change.
 Adding two sounds with the same level produces a combined sound with
a level 3 dB higher than the levels of the individual sounds.
 Adding two sounds, where the difference in level of the two sounds is
greater than or equal to 10 dB produces a combined sound with a level
the same as the louder of the two sounds.
1.2 Noise Metrics
There are a number of different noise metrics that are used in the field of
acoustics to describe the character of noise. For example, the Lmax, fast metric
measures the highest noise level averaged over a period of 125 milliseconds,
whereas the L90, which is typically used to measure background noise, specifies
1
The LAmax metric is also used, but due to its short integration time, it is a much
less reliable indicator of noise levels over a longer duration.
2
Other metrics use different methods to convert data to longer time periods.
For example, Lmax metrics can be converted using a max function. Other
the level of noise which was exceeded for 90% of the measurement duration.
The most common metric used for construction noise is the LAeq metric1
, which
specifies the logarithmically averaged noise level with an A-weighting applied.
The A-weighting is designed to mimic the sensitivities of the human auditory
system to different frequencies of sound. The LAmax metric is also used, but due
to its short integration time, it is a much less informative descriptor of noise
levels measured over a longer duration.
The LAeq metric is used as the metric for assessment of eligibility for noise
insulation and temporary rehousing on the Crossrail Project. This data was
measured at a number of installed monitoring positions by various contractors
operating on the Whitechapel Crossrail sites. The duration of each
measurement was typically one hour, although some contractors measured over
15 minute periods. Where the data was measured in 15 minute periods it is a
simple process of logarithmically averaging each group of four 15 minute
levels to derive the overall one hour measurement2
.
1.3 Data Format
The data from the various monitors was collated during construction on a noise
and vibration monitoring website (see Figure 1), written by the author while
working on the Crossrail project. These measurements are stored in an MS SQL
Database which has fields for each measurement in the MonitoringData table
as listed in Table 2. The identifier fields are linked to other tables in the
database and further data is accessed through table joins in the usual manner.
Field Name Data Type Description
dataId Integer Unique identifier
dataStartTime DateTime Measurement start date / time
dataLength Float Measurement duration
dataDBLevel Float Measured level
dataDBType Integer Metric identifier
dataMonitorID Integer Monitor identifier
dateTimeUploaded DateTime Date and time data was added
Table 2 - Fields in the MonitoringData table of the website SQL database
metrics, such as the L90 can be linearly averaged, although this only
approximates the true level.
Figure 1 - Screenshot from the Crossrail Noise Monitoring website

3. 1.4 Data Conversion
The data from the website was first standardised into one hour durations by
logarithmic averaging using another piece of software written by the author
(see Figure 2), which applies time and level filters to raw data. This was saved
as a single .csv file with fields as listed in Table 3.
Field Data Type Description
StartDateTime DateTime Measurement start date / time
EndDateTime DateTime Measurement end date / time
Duration Float Measurement duration
Level Float Measurement level
Metric String Metric Name
Filter String Filter used to derive the data
from raw format
Monitor String Name of measurement location
Table 3 - Intermediate data format output from VizAcoustics
The third and final stage of conversion was performed using a Python script,
which extracted the LAeq data from the single .csv file and wrote it to separate
.csv files, one for each monitor. The fields of these files are shown in Table 4.
The monitor and duration fields were no longer necessary as each file
represents a single measurement location, and all derived measurements are of
one hour duration.
Field Data Type Description
Level Float Measured level
Year Integer Year of measurement
Month Integer Month of measurement
Day Integer Day of measurement
Hour Integer Start hour of measurement
Table 4 - Fields of .csv files used for analysis in Noise Cluster program
1.5 Data Description
Ten measurement locations were available for analysis. However, due to time
constraints, three locations were selected to illustrate and test the methodology.
These are shown in Figure 3. Measurements were available during different
date periods at each monitor, so a date period of 27th
February 2012 to 30th
November 2013 was chosen, as data was available at all three locations during
this period.
Figure 3 - Aerial View of Whitechapel Worksites and Selected
Measurement Locations (imagery from Google Maps)
1.5.1 Albion Yard (East)
The Albion Yard (East) measurement location is located on the site boundary
of the eastern Cambridge Heath Shaft Worksite at ground floor level. It is
located to monitor noise to the Albion Medical Centre and the east side of the
Albion Yard residential building to the south on Whitechapel Road. Its primary
noise sources in the absence of construction noise are Whitechapel Road and
Cambridge Heath Road.
1.5.2 Albion Yard (North)
The Albion Yard (North) measurement location is located on the site boundary
of the western Cambridge Heath Shaft Worksite at first floor level. It is located
to monitor noise to the north side of the Albion Yard residential building. Its
primary noise sources in the absence of construction noise are Brady Street and
Cambridge Heath Road.
1.5.3 Trinity Hall (East)
The Trinity Hall (East) measurement location is located on the eastern façade
of the Trinity Hall residential building, overlooking the Durward Street Shaft
and Whitechapel Station worksites. It is located on the rooftop of the building
(fourth floor level). Its primary noise sources in the absence of construction
noise are Whitechapel Station and Whitechapel Road.
2 ANALYSIS TASKS
2.1 Clustering
The tasks used in the analysis relate primarily to the first case study chosen for
the literature review [8]. The goal of the case study was to analyse daily
patterns of power consumption at the Energy Research Centre of the
Netherlands. The goal of this analysis was slightly more nuanced in that after
identification of common daily or part-daily patterns (called “profiles” in this
report), it was necessary to try to find likely causes for some of them (e.g.
construction, bank holidays) and exclude them from analysis, so that the true
underlying typical ambient levels could be established. This required the
incorporation of some additional functionality, such as specification of the time
period over which to perform the clustering as shown in Figure 4.
The clustering method used was hierarchical agglomerative clustering, as it
allows for changing the number of clusters in real time and is allows for more
interactive than other types when processing power is limited.
Figure 2 - Screenshot of VizAcoustics noise processing software

4. Figure 4 - Clustering Settings Controls from the Noise Cluster program
for the Crossrail IP D9 Periods 1 – 3
2.2 Visualisation and Analysis of Clusters
The screenshots in the case study only showed visualisation of clusters via the
calendar view and the cluster representatives line graph. In order to gain further
insight into the nature of clusters, the following additional views were
included:
 Distance between clusters, and change in the inter-cluster distance
gradient for each number of partitions in the clustering.
 Number of members of each cluster.
 Number of members of each cluster over each month of the clustering.
2.3 Cluster modification
Although members of clusters (profiles) are by definition more similar to each
other than to member of other clusters, there are additional restrictions imposed
by the noise insulation policy, (e.g. weekday levels should not include bank
holidays), that necessitated the ability to remove members of clusters that fell
on these days. These days were removable by clicking on them whilst holding
the control key. This functionality, in conjunction with the profiles view, also
allows users to remove specific members from a cluster if they are considered
to be outliers in comparison to other members of the cluster.
2.4 Cluster Correlation
The case study indicated that next steps for the program would enable users to
study “several variables simultaneously in order to study correlations between
variables, either manually or automatically”. Functionality was added to the
Noise Cluster program to export two types of cluster representatives, which
were then analysed offline using Python scripts. These representative-types are
as follows:
 Best-fit profile using polynomial curve-fitting.
 Modal profile using the most frequently occurring levels at each hour.
3 ANALYSIS METHODOLOGY
The analysis methodology was inspired by the case study and modified
according to the overall aim of the analysis. Some of the methods used were
identical or very similar, such as:
 Visualisation of the individual profiles.
 Clustering of the data.
 Selection of appropriate clusters.
 Subsequent cluster analysis by correlation.
Other additional steps were introduced, both to better facilitate the analysis and
to improve interactivity. These steps are described in the following subsections.
3.1 Clustering Settings
The clustering settings are set by the user before loading the data. The settings
are as given in Table 5:
Setting Description
Method Clustering method to use (single, complete, average,
weighted, centroid, median, ward)
First Date The first date of the clustering period
Last Date The last date of the clustering period
Start Hour The start hour on each day of clustering
End Hour The end hour on each day of clustering (if the end hour
is less than or equal to the start hour then this falls on the
next day)
Days Days of the week from which clustering is calculated
Table 5 - Clustering settings configurable by the user for each clustering
3.2 Clustering
The clustering is performed by the computer on initial loading of the data, and
upon user request. Reclustering is required when the user wants to change the
clustering method, or alter the time or date periods or days of the week over
which the clustering is performed. Reclustering takes between 2 and 10
seconds on the author’s relatively low specification laptop for the full date
range, depending on the number of days of the week clustered, and the length
of time period for each day.
3.3 Cluster Visualisation
Once the clustering calculation has been completed, the computer sets the
number of clusters to one, and colours each day of the calendar in blue,
indicating that they all belong to the same unified cluster. The Clusters tab
graphs are updated with the cluster distances and gradient changes, the cluster
representative, the number of members and the monthly distribution over the
clustering date range. The computer also highlights all public holidays in a bold
italic font, so they are more easily distinguishable from other days.
3.4 Selection of Correct Number of Clusters
The user increases the number of clusters, using the spinbox in the
Visualisation Controls toolbox, either by clicking the up and down arrows or
by typing in the number directly. This second option can save time if there is a
noticeable sharp change in the Inter-Cluster Distance, or a peak in the Inter-
Cluster Distance Gradient Change at a specific number of clusters, because the
computer needs to update the cluster graphs and calendar view colours each
time the number of clusters is changed.
The correct number of clusters needs to be determined by the user. This
involves consideration of a number of factors, such as:
 Domain knowledge about the likely ambient profile, taking into account
the ambient noise sources in the area
 Changes in the distance between cluster members for each cluster
 Shapes of profiles in the clustering, and the number of days on which
each cluster profile occurs.
 Monthly and seasonal distributions of each cluster
 The shapes of individual profiles within each cluster
 The temporal proximity of individual profiles to public holidays
Figure 5 shows a typical view after increasing the number of clusters to five.
Figure 5 - Initial Clustering of IP D9 Time Periods 1-3. The most likely
ambient levels are represented by the blue cluster

5. 3.5 Removal of cluster members
Outliers may be detected in a cluster by inspection of the individual profiles
for each cluster. These can subsequently be removed by control-clicking on the
day in the calendar view, or by simply increasing the number of clusters until
the outlier separates from the cluster of interest. This needs to be done by the
user as it can be a matter of judgement as to what constitutes an outlier. The
user can also consult other documents, such as project work diaries and other
publicly available information to ascertain the causes of anomalous profiles.
Figure 6 - Profiles of the ambient cluster selected, with bank holidays
deselected manually by the user in the calendar view
3.6 Choosing Cluster Representatives
The Cluster Representatives graph on the Clusters tab, shows the mean level
for each cluster over each hour of the day. However, when cluster members are
removed from consideration by the user, this plot does not update because it
would involve recomputation of the entire clustering. To address this issue, the
user can visualise the data for all the selected days on the Cluster
Representative tab.
Since the goal of the analysis is to find the typical ambient levels, not the
average ambient levels, it was decided not to use the mean or median levels in
this plot. Two other approaches were used instead.
The first approach is to fit a polynomial curve to the data to generate a line of
best-fit that minimises the squared error between the curve and the measured
points for each hour. The order of the polynomial is three by default, but can
be changed by the user by using the spinbox provided in the Visualisation
Controls toolbox.
The second approach is to find the modal level for each hour of the day. This
is achieved by putting each level into a histogram bin and finding the bin with
the most members. The binning interval is 0.1 dB by default, but can be
modified by the user in 0.1 dB increments by using a spinbox in the
Visualisation Controls toolbox.
Once the cluster representatives have been chosen, the user exports them to
.csv files for subsequent analysis. Figure 7 shows a view of polynomial and
modal best-fit lines.
Figure 7 - Polynomial and modal best-fit lines for ambient cluster
members. The backdrop shows the distribution of noise levels for each
hour in 1dB bins
4 IMPLEMENTATION
All of the analysis, with the exception of the correlation, was performed in the
Noise Cluster program, which was modified and improved as the analysis was
conducted and shortcomings were identified. This section describes the
program and its constituent parts, and the standard libraries that were used to
assist in calculations and visualisations.
4.1 Software Development Environment
The environment chosen to develop the software was Eclipse, using the PyDev
add-on. Toolboxes and layouts were designed using the Qt Designer program.
Standard “widgets” were used for most views, apart from the Clustering
Calendar View, which was inherited from a QTableView. The programming
paradigm used was a variation on the Model-View-Presenter pattern. UML
Class diagrams for the views and view models are shown in Figure 8 and Figure
9.
Figure 8 - UML Class Diagram for View Classes
Figure 9 - UML Class Diagram for ViewModel Classes
4.2 Standard Modules
The following Python modules were used to assist in implementation of the
program:
Module Use
PyQt4 Graphical User Interface
Event Handling (signals and slots)
numpy Array handling and manipulation
pandas Internal data representation
Data file import / export
datetime, dateutil Date handling and offsetting
pickle Loading and saving of settings files
math Rounding of numbers for binning
matplotlib Plots
scipy Clustering
Polynomial approximations
Table 6 - List of standard Python modules used in Noise Cluster

6. 4.3 Custom Modules
A number of custom modules were written to assist in the development of the
software. A list of these modules and their use is given in Table 7.
Module Use
dataFrameOperations Counting and returning annual, monthly, daily
and hourly measurements
Returning measurement date ranges
dateOperations Returning days in a date range, names of days
of the week and public holidays
figures Generating plots used in the program
globals Default settings and colours
Table 7 - List of custom Python modules used in Noise Cluster
4.4 Classes
Several clustering classes were written in Python before the application was
developed, and subsequently incorporated into the Noise Cluster program and
modified as required. The UML class diagram is shown in Figure 10.
Figure 10 - UML Diagram of Noise Clustering Classes
5 ANALYSIS PROCESS
The analysis process was iterative in the sense that as information was
discovered through exploration, about the nature of the clusters, more
functionality was incorporated into the program in order to better analyse the
data, such as saving of settings files or generating new types of plots. This
section includes screenshots from throughout the development of the software
and analysis so it may be possible to see when new features were added.
Once a full analysis had been completed for a single measurement location, it
was possible to repeat it at the other two locations in a fairly short time period.
The following subsections describe facets of the analysis process with
annotated plots. The key to the plots is as follows:
(A) is the Clustering Settings Controls toolbar
(B) is the Visualisation Controls toolbar
(C) is the Clustering Calendar view
(D) is the Inter-Cluster Distance and ICD Gradient Change plot
(E) is the Cluster Representatives plot
(F) shows the number of members of each cluster
(G) shows the number of members of each cluster per month
(H) is the profile time history plot
5.1 Initial Clustering
Figure 11 shows the view after the initial clustering has been done and the
number of clusters has been changed by the user for the Albion Yard (North)
measurement location. The number of clusters has been set at a number where
the distance between clusters begins to change at a slower rate. This is
illustrated in (D) by a change in direction in the dark blue Inter-Cluster
Distance line, and a peak in the green ICD Gradient Change line, which is the
change in the derivative of the Inter-Cluster Distance line. The colours of the
lines in (D) do not correspond with any of the other plots. (C), (E), (F) and (G)
show the distribution of each cluster, by day of the year, mean levels, total
number of members and number of members per month, respectively.
The time periods chosen for the analysis corresponded roughly to the periods
in IP D9, with the start-up and shut-down periods joined to the core
construction periods, because curve fitting requires at least two time points.
Figure 11 - View of Clusters for Crossrail Periods 1-3 after Initial
Clustering
5.2 Deselection of Public Holidays
Figure 12 shows an expanded view of the cluster calendar with all the profiles
except public holidays selected. This is accomplished by first selecting a single
member of the cluster, then clicking on the Select Profiles in Same Cluster
button, and finally control-clicking the public holidays in the Calendar view.
Here, (C) is as before and (H) is the time history of each profile over the course
of their respective days. It would have been possible to automate the
deselection, but there are occasions where it is also desirable to deselect days
immediately preceding and following public holidays, so this was left to the
user.
Figure 12 - View of Noise Profiles in a Cluster after Deselecting Public
Holidays
5.3 Step-Changes in Ambient Noise Level
Figure 13 shows a stage in the analysis for IP D9 Period 10. (G) illustrates that
there are two large clusters describing the noise profile on Saturday nights. It
is likely that the second, louder, level shown in (E) is due to the installation of
some static item of plant, so the lower level from the pink cluster was selected
as the ambient level in this case.

7. Figure 13 - Step-Change in Typical Ambient Noise Level
5.4 Inherent Variation in Ambient Noise Levels
For some time periods, the analysis showed a high degree of inherent variation
in the natural ambient noise level, independent of any construction noise
contribution. This can be seen in Figure 14 by the similarly shaped profiles for
the IP D9 Period 12 Sunday night-time period in (E). In this case, the yellow
level was selected as the typical cluster, because it first occurs at the start of
the clustering (G), is similarly persistent to the pink cluster and is at a lower
level so errs on the side of caution when dealing with third parties.
However this does pose the question as to whether there is truly a “typical”
ambient noise level for some time periods or whether it varies as new sources
are introduced. This is more likely to happen at quieter time periods such as
Sunday night times because the overall ambient noise level is lower and
therefore prone to being more affected by other sources due to the logarithmic
summing of noise.
Figure 14 - Clustering for Sunday nights shows four potential "typical"
ambient noise level profiles
5.5 Influence of Local Sources on Typical Noise Levels
It was found whilst analysing the Trinity Hall East measurement location that
typical ambient levels were much easier to establish than at the Albion Yard
locations. This is because it directly overlooks two railway lines. Since
railways have regular schedules with specific numbers of trains and
announcements per hour, the hourly noise levels are much more consistent than
at other locations.
The orange cluster in Figure 15 is by far the most frequently occurring one (F).
The blue and pink clusters represent periods where there is either a reduced or
cancelled service due to public holidays (C). This raises the issue that there
may not be a single typical level for public holidays adjacent to certain types
of environmental noise source.
Figure 15 - Distribution of Typical Ambient Noise Levels adjacent to the
Railway
5.6 Selection of Cluster Representatives
As previously discussed, two methods were used to select representative levels
for ambient clusters. Each one has its strengths and weaknesses.
5.6.1 Polynomial Curve Fitting
The polynomial curve fitting method has the advantage that by specifying the
order of the polynomial, the user can tailor the shape of the curve to have as
many changes in direction as desired. The disadvantage of this approach is that
it could be criticised as a somewhat arbitrary method of selecting the ambient
level; two different experts could easily come up with different levels, which
would not be beneficial for establishing agreement between parties with
differing priorities.
In addition to this, it was found during experimentation that when longer time
periods are clustered, the polynomial curve fitting approach may fit the data
well at the hours of the clustering, but that it does not behave well inbetween
or outside these periods, as shown in Figure 16. This means that when clusters
are joined together, the joins can be very obvious, which gives them an artificial
look, even if the underlying mean squared error between the curve and the
levels has been reduced.
Figure 16 - Deviation of Polynomial Approximation outside Hours of
Clustering
5.6.2 Modal Levels
One of the advantages of the modal levels approach is that there is a direct
analogy between the modal level and the typical level, so it is easier to justify
to residents and other laypeople who may be interested in the analysis why it
has been selected. Although there is a loss of accuracy from binning levels, it
is common practice to report measured levels to the nearest decibel. Therefore,
as long as the bin size is less than or equal to 1 dB, then there is no loss in
reported accuracy. Also, Type 1 sound level meters (the most accurate class)
are only required to be accurate to ± 1 dB, and Type 2 to the ± 2 dB, so the
measurement error is likely to be higher than the analysis error.

8. One of the major disadvantages is that for smaller bin sizes, the number of
levels in each bin is small, so it is more difficult to make the case for the
selected level being typical. For larger bin sizes, where the size is less than 1
dB, the modal level is less smooth in appearance than the fitted line, and can
be subject to sudden apparent “jumps”. This phenomenon will be discussed
further in Section 6, but can be seen at 12pm in Figure 17 below, where the
typical level appears to jump up to around 64 dB, even though there is a
relatively highly populated bin around 60 dB which would make a smoother
profile.
Figure 17 - Illustration of the 'jump' phenomenon for modally derived
noise profiles
6 RESULTS AND CONCLUSIONS
6.1 Results
6.1.1 Noise Profiles
Noise profiles for the three locations analysed on Weekdays, Saturdays and
Sundays derived from best-fit lines and modal levels are shown in Figure 18
and Figure 19.
Figure 18 - Ambient noise profiles derived from best-fit lines
Figure 19 - Ambient noise profiles derived from modal levels
Overall, the noise profiles represent good and similar estimations of the
ambient noise levels without the presence of construction noise. However, due
to the choice to analyse the levels using time windows derived from the noise
insulation policy, there are marked changes in curve gradient at the boundaries
of some of the time periods used in Figure 18, due to the curve fitting algorithm
used. These may indicate some inaccuracies in the estimated noise levels at
certain hours of the day. The profiles derived from the modal levels do not
exhibit such obvious transitions, although they are subject to their own
irregularities, such as the “jump” in noise level at 12pm in the Albion Yard
East Weekdays plot. This happens when there are two competing levels within
a time period, and the one which fits better with the trend is slightly less
populous than the other. On balance, it is considered that the modal
representation is superior because it does not make any prior assumptions on
the noise profile and is more easily explainable to the layman as the “typical”
level as opposed to some mathematical approximation.
6.1.2 Cross Correlation
It would be expected that noise profiles from locations with similar ambient
noise climates would exhibit a higher cross-correlation than areas with
different ambient noise climates. Similar noise climates at two locations may
be due to proximity to each other and consequently to similar local noise
sources, or due to similarity to each other in terms of line of sight to significant
correlated noise sources, such as major roads.
Polynomial Approximation
Table 8 shows the cross-correlation between each position from the profiles
derived using the polynomial approximation technique.
Day Type Location Albion
Yard East
Albion
Yard
North
Trinity
Hall East
Weekdays A.Y.E. 1 0.97 0.95
A.Y.N 0.97 1 0.98
T.H.E 0.95 0.98 1
Saturdays A.Y.E. 1 0.96 0.95
A.Y.N 0.96 1 0.93
T.H.E 0.95 0.93 1
Sundays A.Y.E. 1 0.89 0.92
A.Y.N 0.89 1 0.93
T.H.E 0.92 0.93 1
Table 8 - Cross correlation between profiles derived using polynomial
approximations
Weekday ambient noise level exhibit very high correlations between all
locations. This suggests that noise from typical weekday activities such as road
traffic noise from commuters and commercial deliveries on the nearby A11
(Whitechapel Road) and A107 (Cambridge Heath Road) has a large influence
on ambient noise levels throughout the area.
On Saturdays, the correlation between noise levels at Trinity Hall East and
Albion Yard North is slightly lower. This is likely to be due to Albion Yard
North not having a direct line of sight to Whitechapel Road, which has a

9. number of restaurants, shops, pubs and a market, which contribute to the noise
climate.
On Sundays, noise levels at Trinity Hall East are not well correlated with either
of the Albion Yard locations. This suggests that due to the lower levels of
ambient noise from commercial and leisure activities, local noise sources have
more of an effect on the noise profile in each location. Noise levels at Albion
Yard are also very poorly correlated on Sundays. This is mostly due to the
increase in noise levels around 10pm at Albion Yard East. The cause of this
increase is unknown, but is possibly due to customers using the beer-garden of
the pub adjacent to the monitor.
Modal Levels
Table 9 shows the cross-correlation between the typical noise profiles for each
measurement location derived using modal levels.
Day Type Location Albion
Yard East
Albion
Yard
North
Trinity
Hall East
Weekdays A.Y.E. 1 087 0.88
A.Y.N 0.87 1 0.94
T.H.E 0.88 0.94 1
Saturdays A.Y.E. 1 084 0.90
A.Y.N 0.84 1 0.82
T.H.E 0.90 0.82 1
Sundays A.Y.E. 1 0.82 0.86
A.Y.N 0.82 1 0.88
T.H.E 0.86 0.88 1
Table 9 - Cross correlation between profiles derived using modal levels
It is clear from a quick comparison of the two tables that the correlation
between modal levels is much lower than using the polynomial approximation
technique. This is partially caused by the more “spiky” nature of binned levels,
and the “jump” phenomenon described in 5.6.2 and 6.1.1. However, ignoring
the relative comparison between the two tables and viewing the modal
correlations in isolation, the lowest correlation coefficient between any two
locations is 0.84, which indicates that they are indeed describing similar profile
shapes, as evidenced by the profile plots.
6.2 Conclusions
The advantage of an analysis using long-term measurements is that it is easier
to establish a typical level where one exists. However, the ambient noise
climate in any area is subject to change, for example due to introduction of new
noise sources such as a new shop opening or a road closure in another area
directing traffic to nearby roads. These changes may be independent of the
construction project, or indirectly related to the project. Such changes make it
more difficult to establish the typical ambient noise profile in the location.
However, using the software developed, it is much easier to see when the
changes occurred, whether they are temporary, permanent or seasonal, as well
as what times of day they occur at. This makes it easier to find explanations for
the changes and should therefore make it easier to make a cogent argument to
interested parties about what the typical levels are.
Further, the analysis has found that there may be many levels that could be
judged to be typical of the noise climate at a given location, particularly at
times of the day or night where the overall ambient noise level is low, and
therefore any small local changes have a bigger effect on the noise level.
6.3 Further Work
The analysis is based on assumption that the time periods used in the IP D9
policy are appropriate time periods because they are organised both around the
hours of working, and typical hours of waking and sleeping from other
legislation. However, this assumption may not be correct in the second respect,
as much of the legislation was developed a long time ago before the
introduction of more flexible working hours. A future analysis could use
clustering of different time periods to determine which fall more naturally into
clusters.
Further, if the technique used here were used in advance of construction, i.e.
based on ambient data only, other time periods might be more appropriate for
the purpose of analysis, before arranging the relevant noise levels into the
periods required in the relevant policy document.
There also remain a number of enhancements that could be made to the
program, such as:
 Automatic detection of cluster outliers using rules defined or
configurable by the user
 Add cluster standard deviations or variances to cluster plots for better
interpretation
 Incorporate correlation analysis into the software for faster analysis
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